I'm learning R and trying to use it for a statistical analysis at the same time.
Here, I am in the first part of the work: I am writing matrices and doing some simple things with them, in order to work later with these.
punti<-c(0,1,2,4)
t1<-matrix(c(-8,36,-8,-20,51,-17,-17,-17,57,-19,-19,-19,35,-8,-19,-8,0,0,0,0,-20,-20,-20,60,
-8,-8,-28,44,-8,-8,39,-23,-8,-19,35,-8,57,-8,-41,-8,-8,55,-8,-39,-8,-8,41,-25,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),ncol=4,byrow=T)
colnames(t1) <- c("20","1","28","19")
r1<-matrix(c(12,1,19,9,20,20,11,20,20,11,20,28,0,0,0,12,19,19,20,19,28,15,28,19,11,28,1,
33,20,28,31,1,19,17,28,19,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),ncol=3,byrow=T)
pt1<-rbind(sort(colSums(t1)),sort(punti))
colnames(r1)<-c("Valore","Vincitore","Perdente")
r1<-as.data.frame(r1)
But I have more matrices t_ and r_ so I would like to run a for-loop like:
for (i in 1:150)
{
pt[i]<-rbind(sort(colSums(t[i])),sort(punti))
colnames(r[i])<-c("Valore","Vincitore","Perdente")
r[i]<-as.data.frame(r[i])
}
This one just won't work because r_, t_ and pt_ are strings, but you get both the idea and that I would not like to copy-paste these three lines and manually edit the [i] 150 times. Is there a way to do it?
personally i don't advise dynamically and automatically creating lots of variables in the global environment, and would advise you to think about how you can accomplish your goals without such an approach. with that said, if you feel you really need to dynamically create all these variables, you may benefit from the assign function.
it could work like so:
for (i in 1:150)
{
assign(paste0('p',i),rbind(sort(colSums(t[i])),sort(punti)))
}
the first argument in the assign function is the formula for the variable name and how it is created; the second argument is what you wish to assign to the variable being created.
Related
im super newbie on R and i have been learning for myself for a few weeks already due my work degree.
Im almost done with the statistical analysis that i need, but it is through an ugly and messy code, that is, repeating lot of codes for several data frames, to apply different statistical tests, save results, etc.
Well now, for personal interest, want to write this better, but im totally trapped in my ignorance and really need a push to get the idea, please.
For example, i want to create a function that measure the correlation on all the data tables im using and save those results as a tables using the input name as part of the output name.
I mean, if we had the iris data but measured on different seasons, e.g. iris_fall, iris_winter, iris_spring and iris_summer, after apply cor(X) method to each one, i want to save those results as tables called like "mCoriris_fall.txt", "mCoriris_winter.txt", "mCoriris_spring.txt" and "mCoriris_summer.txt" respectively.
My useless code for now say:
cor_PQ<-function(X) {
cor_PQ<-cor(X, use="pairwise.complete.obs")
return(cor_PQ)
}
savecor<-function(t) {
outputname<-(paste0("mCor",t)) #HOW DO I CALL THE NAME OF THE INPUT? t is cor_PQ result matrix.
savecor<-write.table(t, file=paste0(outputname,".txt"))
return(savecor)
}
cor_PQ(Iris_fall)
I expect to get cor result and save it as a table in my workspace, using the input name as part of the output name.
Im aware this are 2 separates functions and the one to write table should be inside the function for cor(x), but i cant understand how.
I have been reading a lot but i just cant fit all in my head.
Thanks to anyone who can help me.
Regards.
UNTIL HERE IT HAS BEEN SOLVED...
But after making a list with my 14 data frames to apply cor and other methods, the write.table function overwrite the 14 cor results on 1 single doc. This is my code.
PQ_files<-list.files(path="C:/Users/Sol/Documents/ProyectoTÃtulo/CalidadAgua/Matrices/Regs",pattern="\\_PQ.txt")
PQ_data<-lapply(PQ_files, read.table)
names(PQ_data)<-gsub("\\_PQ.txt","", PQ_files)
PQ_data
cor_PQ<-function(X) {
cor_PQ<-cor(X, use="pairwise.complete.obs")
outputname.txt<-paste0("mCor",deparse(substitute(X)),".txt")
write.table(cor_PQ, file=outputname.txt)
outputname.pdf<-paste0("Cor",deparse(substitute(X)),".pdf")
pdf(outputname.pdf)
plot(X)
dev.off()
return(cor_PQ)
}
for (i in seq_along(PQ_data)){
Correlaciones<-lapply(PQ_data,cor_PQ)
}
Correlaciones
On SUM: seems to work almost good, until the write.table and plot(x) overwrite the outputs from the 14 dataframes on my PQ_data withe the name mCor[[i]] and CorX[[i]], respectively.
Should i define [i] somehow to have each results with the right name?
Also, when i run Correlaciones at the end, i can see the cor result for the 14 dataframes in one single dataframe, but i dont know how to split them correctly.
I guess almost there.
THANKS AGAIN!
You can combine the two functions and use deparse substitute to get input names as string
cor_PQ <- function(X) {
cor_PQ<-cor(X, use="pairwise.complete.obs")
outputname<- paste0("mCor",deparse(substitute(X)), ".txt")
write.table(t, file=outputname)
return(cor_PQ)
}
and then call
cor_PQ(Iris_fall)
I have successfully allocated dataframe names and populated them (see code) but I do not know how to subsequently reference them. So I loop through to assign df.test1 and populate it with some data 1 and so on. I know that the df has been created, and can view or summary it in the console, but not in the code.
I am pretty new to R so am not sure if some of the solutions I have looked at apply to me.
num.clusters <- 5
for (i in 1:num.clusters) {
assign(paste("df.test",i,sep=""), paste("somedata", i))
}
This works but Then want to do something like:
View(df.test,i)
to view whatever iteration from 1 to 5.
I want to be able to use the assigned dataframes like any other dataframe. I could hard code this as View(df.test1) but that would defeat the point. I also want to do other things with the datframe, e.g. subsetting.
I know this doesn't work. Would love to know what does.
Many thanks...
Your question is the proof that the approach is problematic: avoid using assign in general because it makes accessing the variables afterwards awkward (among other issues).
A cleaner way is to just put your "data frames" (copying from your example) in a list:
num.clusters <- 5
df.test <- list()
for (i in 1:num.clusters) {
df.test[[i]] <- paste("somedata", i)
}
Then you would just access them like this:
View(df.test[[i]])
If what you put in there was an actual data.frame (and not the strings you were using), you could then access its columns like any other data.frame:
df.test[[i]]$Name
Or
df.test[[i]][, "Name"]
I have a dataset of 80 variables, and I want to loop though a subset of 50 of them and construct returns. I have a list of the names of the variables for which I want to construct returns, and am attempting to use the dplyr command mutate to construct the variables in a loop. Specifically my code is:
for (i in returnvars) {
alldta <- mutate(alldta,paste("r",i,sep="") = (i - lag(i,1))/lag(i,1))}
where returnvars is my list, and alldta is my dataset. When I run this code outside the loop with just one of the `i' values, it works fine. The code for that looks like this:
alldta <- mutate(alldta,rVar = (Var- lag(Var,1))/lag(Var,1))
However, when I run it in the loop (e.g., attempting to do the previous line of code 50 times for 50 different variables), I get the following error:
Error: unexpected '=' in:
"for (i in returnvars) {
alldta <- mutate(alldta,paste("r",i,sep="") ="
I am unsure why this issue is coming up. I have looked into a number of ways to try and do this, and have attempted solutions that use lapply as well, without success.
Any help would be much appreciated! If there is an easy way to do this with one of the apply commands as well, that would be great. I did not provide a dataset because my question is not data specific, I'm simply trying to understand, as a relative R beginner, how to construct many transformed variables at once and add them to my data frame.
EDIT: As per Frank's comment, I updated the code to the following:
for (i in returnvars) {
varname <- paste("r",i,sep="")
alldta <- mutate(alldta,varname = (i - lag(i,1))/lag(i,1))}
This fixes the previous error, but I am still not referencing the variable correctly, so I get the error
Error in "Var" - lag("Var", 1) :
non-numeric argument to binary operator
Which I assume is because R sees my variable name Var as a string, rather than as a variable. How would I correctly reference the variable in my dataset alldta? I tried get(i) and alldta$get(i), both without success.
I'm also still open to (and actively curious about), more R-style ways to do this entire process, as opposed to using a loop.
Using mutate inside a loop might not be a good idea either. I am not sure if mutate makes a copy of the data frame but its generally not a good practice to grow a data frame inside a loop. Instead create a separate data frame with the output and then name the columns based on your logic.
result = do.call(rbind,lapply(returnvars,function(i) {...})
names(result) = paste("r",returnvars,sep="")
After playing around with this more, I discovered (thanks to Frank's suggestion), that the following works:
extended <- alldta # Make a copy of my dataset
for (i in returnvars) {
varname <- paste("r",i,sep="")
extended[[varname]] = (extended[[i]] - lag(extended[[i]],1))/lag(extended[[i]],1)}
This is still not very R-styled in that I am using a loop, but for a task that is only repeating about 50 times, this shouldn't be a large issue.
I wrote a function in R - called "filtre": it takes a dataframe, and for each line it says whether it should go in say bin 1 or 2. At the end, we have two data frames that sum up to the original input, and corresponding respectively to all lines thrown in either bin 1 or 2. These two sets of bin 1 and 2 are referred to as filtre1 and filtre2. For convenience the values of filtre1 and filtre2 are calculated but not returned, because it is an intermediary thing in a bigger process (plus they are quite big data frame). I have the following issue:
(i) When I later on want to use filtre1 (or filtre2), they simply don't show up... like if their value was stuck within the function, and would not be recognised elsewhere - which would oblige me to copy the whole function every time I feel like using it - quite painful and heavy.
I suspect this is a rather simple thing, but I did search on the web and did not find the answer really (I was not sure of best key words). Sorry for any inconvenience.
Thxs / g.
It's pretty hard to know the optimum way of achieve what you want as you do not provide proper example, but I'll give it a try. If your variables filtre1 and filtre2 are defined inside of your function and you do not return them, of course they do not show up on your environment. But you could just return the classification and make filtre1 and filtre2 afterwards:
#example data
df<-data.frame(id=1:20,x=sample(1:20,20,replace=TRUE))
filtre<-function(df){
#example function, this could of course be done by bins<-df$x<10
bins<-numeric(nrow(df))
for(i in 1:nrow(df))
if(df$x<10)
bins[i]<-1
return(bins)
}
bins<-filtre(df)
filtre1<-df[bins==1,]
filtre2<-df[bins==0,]
I'm trying to subset a dataframe within a function using a mixture of fixed variables and some variables which are created within the function (I only know the variable names, but cannot vectorise them beforehand). Here is a simplified example:
a<-c(1,2,3,4)
b<-c(2,2,3,5)
c<-c(1,1,2,2)
D<-data.frame(a,b,c)
subbing<-function(Data,GroupVar,condition){
g=Data$c+3
h=Data$c+1
NewD<-data.frame(a,b,g,h)
subset(NewD,select=c(a,b,GroupVar),GroupVar%in%condition)
}
Keep in mind that in my application I cannot compute g and h outside of the function. Sometimes I'll want to make a selection according to the values of h (as above) and other times I'll want to use g. There's also the possibility I may want to use both, but even just being able to subset using 1 would be great.
subbing(D,GroupVar=h,condition=5)
This returns an error saying that the object h cannot be found. I've tried to amend subset using as.formula and all sorts of things but I've failed every single time.
Besides the ease of the function there is a further reason why I'd like to use subset.
In the function I'm actually working on I use subset twice. The first time it's the simple subset function. It's just been pointed out below that another blog explored how it's probably best to use the good old data[colnames()=="g",]. Thanks for the suggestion, I'll have a go.
There is however another issue. I also use subset (or rather a variation) in my function because I'm dealing with several complex design surveys (see package survey), so subset.survey.design allows you to get the right variance estimation for subgroups. If I selected my group using [] I would get the wrong s.e. for my parameters, so I guess this is quite an important issue.
Thank you
It's happening right as the function is trying to define GroupVar in the beginning. R is looking for the object h by itself (not within the dataframe).
The best thing to do is refer to the column names in quotes in the subset function. But of course, then you'd have to sidestep the condition part:
subbing <- function(Data, GroupVar, condition) {
....
DF <- subset(Data, select=c("a","b", GroupVar))
DF <- DF[DF[,3] %in% condition,]
}
That will do the trick, although it can be annoying to have one data frame indexing inside another.